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=== Slides === [http://curtis.ml.cmu.edu/w/courses/images/d/dc/Lecture22-SN.pdf Slides in PDF] === Readings === E. Airoldi, D. Blei, S. Fienberg, and E. P. Xing,[http://www.sailing.cs.cmu.edu/pdf/2008/airoldijmlr08.pdf Mixed Membership Stochastic Blockmodel] Journal of Machine Learning Research, 9(Sep):1981--2014, 2008. === Taking home message === * Networks can be described by global/local features (what are they?) * But these features are not helpful for inferring individual nodal information. * SBM is a probabilistic model for grouping network nodes into communities/clusters, each cluster corresponds to a single "role" or social position * MMSB is a probabilistic model that allows each node to have multiple roles, and allow each edge to have contextual-dependent role instantiations. * dMMSB can infer multi-role trajectories for every node. * Inference of MMSB and dMMSB employs MCMC or VI

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